Magnetopause Detection under Low Solar Wind Density Based on Deep Learning

Abstract

Extracting the peak value of the X-ray signal in the original magnetopause detection method of soft X-ray imaging (SXI) for the SMILE satellite is problematic because of the unclear interface of the magnetosphere system under low solar wind density and the short integration time. Herein, we propose a segmentation algorithm for soft X-ray images based on depth learning, we construct an SXI simulation dataset, and we segment the magnetospheric system by learning the spatial structure characteristics of the magnetospheric system image. Then, we extract the maximum position of the X-ray signal and calculate the spatial configuration of the magnetopause using the tangent fitting approach. Under a uniform universe condition, we achieved a pixel accuracy of the maximum position of the photon number detected by the network as high as 90.94% and contained the position error of the sunset point of the 3D magnetopause below 0.2 RE. This result demonstrates that the proposed method can detect the peak photon number of magnetospheric soft X-ray images with low solar wind density. As such, its use improves the segmentation accuracy of magnetospheric soft X-ray images and reduces the imaging time requirements of the input image.